{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:CVS32SIJZ2DAXHC2A5IMGDIESI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ca9b6ac516b3a8009170f102649b1d43bf01215bb269f6e99723bdd2a2f27ee7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-31T03:35:59Z","title_canon_sha256":"45b09311074c06d061b7280cb023fe79256b3b9b501f568dbcc574d2fc0c033e"},"schema_version":"1.0","source":{"id":"2310.20150","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.20150","created_at":"2026-07-05T07:07:24Z"},{"alias_kind":"arxiv_version","alias_value":"2310.20150v1","created_at":"2026-07-05T07:07:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.20150","created_at":"2026-07-05T07:07:24Z"},{"alias_kind":"pith_short_12","alias_value":"CVS32SIJZ2DA","created_at":"2026-07-05T07:07:24Z"},{"alias_kind":"pith_short_16","alias_value":"CVS32SIJZ2DAXHC2","created_at":"2026-07-05T07:07:24Z"},{"alias_kind":"pith_short_8","alias_value":"CVS32SIJ","created_at":"2026-07-05T07:07:24Z"}],"graph_snapshots":[{"event_id":"sha256:c46abbc90cbb1cca00fb54d04526427a4a4d5262153ce5a798bf71cb7d3cf373","target":"graph","created_at":"2026-07-05T07:07:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2310.20150/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) have achieved significant progress from pre-training on and memorizing a wide range of textual data, however, this process might suffer from privacy issues and violations of data protection regulations. As a result, the ability to easily remove data related to individual users from such models while not deteriorating their predictive quality after the removal becomes increasingly important. To address these issues, in this work, we propose an efficient unlearning framework that could efficiently update LLMs without having to retrain the whole model after data remov","authors_text":"Diyi Yang, Jiaao Chen","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-31T03:35:59Z","title":"Unlearn What You Want to Forget: Efficient Unlearning for LLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.20150","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a75e5e396925c87870812ef0208db63521c6888993262ae0622bb0b8b4408e5b","target":"record","created_at":"2026-07-05T07:07:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ca9b6ac516b3a8009170f102649b1d43bf01215bb269f6e99723bdd2a2f27ee7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-31T03:35:59Z","title_canon_sha256":"45b09311074c06d061b7280cb023fe79256b3b9b501f568dbcc574d2fc0c033e"},"schema_version":"1.0","source":{"id":"2310.20150","kind":"arxiv","version":1}},"canonical_sha256":"1565bd4909ce860b9c5a0750c30d0492156428bc90eaf115b716422e2669ba42","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1565bd4909ce860b9c5a0750c30d0492156428bc90eaf115b716422e2669ba42","first_computed_at":"2026-07-05T07:07:24.809786Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:07:24.809786Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GnSsJBs+PZBdqn2OoXbEkLdjxkj72QB/XvtRg5hbIsn3/BQU3ApUo/KRbJdNeAtagTBi7mDAUu6rkk/2xOs2Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:07:24.810255Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.20150","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a75e5e396925c87870812ef0208db63521c6888993262ae0622bb0b8b4408e5b","sha256:c46abbc90cbb1cca00fb54d04526427a4a4d5262153ce5a798bf71cb7d3cf373"],"state_sha256":"0619bcbf29f74a3bd8709cc835613a8c12ca015494d290b2e1b47237e8886a7e"}